Výsledky vyhledávání - "Ammar Mohemmed"

Training spiking neural networks to associate spatio-temporal input–output spike patterns

Autor: Satoshi Matsuda, Ammar Mohemmed, Stefan Schliebs, Nikola Kasabov

Publikováno v: Neurocomputing. 107:3-10

In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input-output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotempora

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f75bdb172acaf6210810255c75b99c84
https://doi.org/10.1016/j.neucom.2012.08.034

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Coverage Maximization and Energy Conservation for Mobile Wireless Sensor Networks

Autor: Kamarulzaman Ab. Aziz, Ammar Mohemmed, Nor Azlina Ab Aziz, Syabeela Syahali, Mohamad Yusoff Alias

Publikováno v: BIC-TA

WSN is a group of low-cost, low-power, multifunctional and small size wireless sensor nodes that work together to sense the environment, perform simple data processing and communicate wirelessly over a short distance. Mobile wireless sensor networks

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a5a4ba64932106b8282e54060eee6e58
https://doi.org/10.4018/jncr.2012040103

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Particle swarm optimisation based AdaBoost for object detection

Autor: Mengjie Zhang, Ammar Mohemmed, Mark Johnston

Publikováno v: Soft Computing. 15:1793-1805

This paper proposes a new approach to using particle swarm optimisation (PSO) within an AdaBoost framework for object detection. Instead of using exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::21bd9f834aa1839f214cc2b239c6ca2b
https://doi.org/10.1007/s00500-010-0615-x

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Hybrid co-evolutionary particle swarm optimization and noising metaheuristics for the delay constrained least cost path problem

Autor: N. C. Sahoo, Ammar Mohemmed, Tan Kim Geok

Publikováno v: Journal of Heuristics. 16:593-616

This paper presents a co-evolutionary particle swarm optimization (PSO) algorithm, hybridized with noising metaheuristics, for solving the delay constrained least cost (DCLC) path problem, i.e., shortest-path problem with a delay constraint on the to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::aaf2c6ce6a3d75a3a59a55c7f565cc0a
https://doi.org/10.1007/s10732-009-9109-3

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Solving shortest path problem using particle swarm optimization

Autor: Tan Kim Geok, N. C. Sahoo, Ammar Mohemmed

Publikováno v: Applied Soft Computing. 8:1643-1653

This paper presents the investigations on the application of particle swarm optimization (PSO) to solve shortest path (SP) routing problems. A modified priority-based encoding incorporating a heuristic operator for reducing the possibility of loop-fo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::053eb094a2378d279c4e77c66a759ace
https://doi.org/10.1016/j.asoc.2008.01.002

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Incremental learning algorithm for spatio-temporal spike pattern classification

Autor: Ammar Mohemmed, Nikola Kasabov

Publikováno v: IJCNN

In a previous work (Mohemmed et al. [11]), the authors proposed a supervised learning algorithm to train a spiking neuron to associate input/output spike patterns. In this paper, the association learning rule is applied in training a single layer of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a96d1b3d91f6a18502ca0783d0d04bd7
https://doi.org/10.1109/ijcnn.2012.6252533

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Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modelling

Autor: Ammar Mohemmed, Nikola Kasabov, Stefan Schliebs

Publikováno v: Computational Intelligence Methods for Bioinformatics and Biostatistics ISBN: 9783642356858
CIBB

Computational neuro-genetic models (CNGM) combine two dynamic models – a gene regulatory network (GRN) model at a lower level, and a spiking neural network (SNN) model at a higher level to model the dynamic interaction between genes and spiking pat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::deb6b62551b9de41027cfec74435a991
https://doi.org/10.1007/978-3-642-35686-5_1

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Evaluating SPAN Incremental Learning for Handwritten Digit Recognition

Autor: Ammar Mohemmed, Guoyu Lu, Nikola Kasabov

Publikováno v: Neural Information Processing ISBN: 9783642344862
ICONIP (3)

In a previous work [12, 11], the authors proposed SPAN: a learning algorithm based on temporal coding for Spiking Neural Network (SNN). The algorithm trains a neuron to associate target spike patterns to input spatio-temporal spike patterns. In this

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1da2cef5c1b7a4786eeec742b2d1effc
https://doi.org/10.1007/978-3-642-34487-9_81

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Method for Training a Spiking Neuron to Associate Input-Output Spike Trains

Autor: Ammar Mohemmed, Satoshi Matsuda, Nikola Kasabov, Stefan Schliebs

Publikováno v: Engineering Applications of Neural Networks ISBN: 9783642239564
EANN/AIAI (1)

We propose a novel supervised learning rule allowing the training of a precise input-output behavior to a spiking neuron. A single neuron can be trained to associate (map) different output spike trains to different multiple input spike trains. Spike

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97c04648ab791704629c907799cd83bf
https://doi.org/10.5167/uzh-60762

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